Kabita Chatterjee

Bio: Kabita Chatterjee is an academic researcher. The author has contributed to research in topics: Cancer & Mitochondrial fission. The author has an hindex of 3, co-authored 4 publications receiving 40 citations.

Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer.

Abstract: FTIR spectroscopy and Raman spectroscopy of biological analytes are increasingly explored as screening tools for early detection of cancer. In the present study, an integrated analysis of the FTIR and Raman spectra obtained from exfoliated cells is adopted to improve discrimination of normal, pre-cancerous and cancerous conditions. Multiple spectra were obtained from 13 normal, 13 pre-cancer and 10 cancer patients in both modes. Compared to normal patients, significant differences were observed at 1550, 1580, 1640, 2370, 2330, 2950-3000 and 3650-3750 cm-1 (FTIR) and 520, 640, 785, 827, 850, 935, 1003, 1175, 1311 cm-1 and 1606 cm-1 (Raman) vibrations of the other two. The increase in DNA, protein and lipid content with malignancy was more clearly elucidated by examining both spectra. Principal component analysis (PCA)-linear discriminant analysis (LDA) with 10-fold cross validation of the FTIR and Raman spectral data sets showed efficient discrimination between normal and pathological conditions while overlapping was seen between the two pathologies. The PCA-LDA model of the dual spectra yielded a classification accuracy of 98% in comparison with either FTIR (85%) or Raman (82%) in a spectrum-wise comparison. In the patient-wise approach (mean of all spectra from a patient), the overall classification efficiency was 73%, 80% and 87% for FTIR, Raman and integrated spectral approaches respectively. Moreover, the efficiency of the integrated FTIR-Raman PCA-LDA model as a prediction tool was tested to screen susceptible individuals (11 cigarette smokers) using the dual spectra acquired from these individuals. The study presents proof-of-concept for adopting a large-scale, follow-up trial of the approach for mass screening purposes.

Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.

Abstract: Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

Abstract: Many problems exist within the myriad of currently employed screening and diagnostic methods. Further, an incredibly wide variety of procedures are used to identify an even greater number of diseases which exist in the world. There is a definite unmet clinical need to improve diagnostic capabilities of these procedures, including improving test sensitivity and specificity, objectivity and definitiveness, and reducing cost and invasiveness of the test, with an interest in replacing multiple diagnostic methods with one powerful tool. There has been a recent surge in the literature which focuses on utilizing Raman spectroscopy in combination with machine learning analyses to improve diagnostic measures for identifying an assortment of diseases, including cancers, viral and bacterial infections, neurodegenerative and autoimmune disorders, and more. This review highlights the work accomplished since 2018 which focuses on using Raman spectroscopy and machine learning to address the need for better screening and medical diagnostics in all areas of disease. A critical evaluation considers both the benefits and obstacles of utilizing the method for universal diagnostics. It is clear based on the evidence provided herein Raman spectroscopy in combination with machine learning provides the first glimmer of hope for the development of an accurate, inexpensive, fast, and non-invasive method for universal medical diagnostics.

Oral cancer diagnosis and perspectives in India.

Abstract: Globally, oral cancer is the sixth most common type of cancer with India contributing to almost one-third of the total burden and the second country having the highest number of oral cancer cases. Oral squamous cell carcinoma (OSCC) dominates all the oral cancer cases with potentially malignant disorders, which is also recognized as a detectable pre-clinical phase of oral cancer. Tobacco consumption including smokeless tobacco, betel-quid chewing, excessive alcohol consumption, unhygienic oral condition, and sustained viral infections that include the human papillomavirus are some of the risk aspects for the incidence of oral cancer. Lack of knowledge, variations in exposure to the environment, and behavioral risk factors indicate a wide variation in the global incidence and increases the mortality rate. This review describes various risk factors related to the occurrence of oral cancer, the statistics of the distribution of oral cancer in India by various virtues, and the socio-economic positions. The various conventional diagnostic techniques used routinely for detection of the oral cancer are discussed along with advanced techniques. This review also focusses on the novel techniques developed by Indian researchers that have huge potential for application in oral cancer diagnosis.

Klf5 down-regulation induces vascular senescence through eIF5a depletion and mitochondrial fission.

Abstract: Although dysregulation of mitochondrial dynamics has been linked to cellular senescence, which contributes to advanced age-related disorders, it is unclear how Kruppel-like factor 5 (Klf5), an essential transcriptional factor of cardiovascular remodeling, mediates the link between mitochondrial dynamics and vascular smooth muscle cell (VSMC) senescence. Here, we show that Klf5 down-regulation in VSMCs is correlated with rupture of abdominal aortic aneurysm (AAA), an age-related vascular disease. Mice lacking Klf5 in VSMCs exacerbate vascular senescence and progression of angiotensin II (Ang II)-induced AAA by facilitating reactive oxygen species (ROS) formation. Klf5 knockdown enhances, while Klf5 overexpression suppresses mitochondrial fission. Mechanistically, Klf5 activates eukaryotic translation initiation factor 5a (eIF5a) transcription through binding to the promoter of eIF5a, which in turn preserves mitochondrial integrity by interacting with mitofusin 1 (Mfn1). Accordingly, decreased expression of eIF5a elicited by Klf5 down-regulation leads to mitochondrial fission and excessive ROS production. Inhibition of mitochondrial fission decreases ROS production and VSMC senescence. Our studies provide a potential therapeutic target for age-related vascular disorders.